1. Technical Field
The present invention relates to data mining in general, and, in particular, to a method and system for providing machine learning. Still more particularly, the present invention relates to a method and system for generating object classification models.
2. Description of Related Art
The objective of object classification (often referred to as supervised learning) is to find an approximation (or hypothesis) to a target concept that assigns objects (such as processes or events) into different classifications (or classes). Generally speaking, object classification can be divided into two phases, namely, a learning phase and a testing phase.
The goal of a learning phase is to learn how to classify objects by finding correlations among object descriptions. During the learning phase, a learning algorithm is applied to a set of training data that typically includes object descriptions (feature variables) together with the correct classification for each object (class variable) for constructing an object classification model capable of predicting a class variable of a record in which the feature variables are known but the class variable is unknown. Thus, the end result of the learning phase is an object classification model that can be used to predict classes of new objects.
During the testing phase, the object classification model derived in the training phase is utilized to predict the classes of a set of testing objects. The object classes predicted by the object classification model are subsequently compared to the true object classes to determine the accuracy of the object classification model.
While most learning algorithms of conventional object classification systems can produce sufficiently accurate object classification models for many applications, they suffer from a number of limitations. Specifically, the learning algorithms of conventional object classification systems are unable to adapt over time. In other words, once an object classification model had been generated by a learning algorithm, the object classification model cannot be reconfigured based on new experiences. Thus, conventional object classification systems that employ such object classification models are prone to repeating the same errors.
Consequently, it would be desirable to provide an improved object classification system.